# Preliminary Results of a Survey on the Use of Self-Adaptation in Industry

arXiv: https://arxiv.org/abs/2204.06816 · HTML: https://mlsanity.com/paper/2204.06816/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2022.

## Built on by (most cited first, 3 of 3)

- [Towards Agentic Runtime Healing](https://mlsanity.com/paper/2408.01055/) (2024)
- [Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems](https://mlsanity.com/paper/2210.05931/) (2022)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)

## Abstract (arXiv)

Self-adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-adaptation is currently not clear. To get insights into the use of self-adaptation in practice, we are running a large-scale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-adaptive systems. We highlight the main insights obtained so far: motivations for self-adaptation, concrete use cases, and difficulties encountered when applying self-adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study.